Cornish et al. Genome Medicine (2015) 7:95
DOI 10.1186/s13073-015-0212-9
RESEARCH
Open Access
Exploring the cellular basis of human
disease through a large-scale mapping
of deleterious genes to cell types
Alex J. Cornish* , Ioannis Filippis, Alessia David and Michael J.E. Sternberg
Abstract
Background: Each cell type found within the human body performs a diverse and unique set of functions, the
disruption of which can lead to disease. However, there currently exists no systematic mapping between cell types
and the diseases they can cause.
Methods: In this study, we integrate protein–protein interaction data with high-quality cell-type-specific gene
expression data from the FANTOM5 project to build the largest collection of cell-type-specific interactomes created to
date. We develop a novel method, called gene set compactness (GSC), that contrasts the relative positions of
disease-associated genes across 73 cell-type-specific interactomes to map genes associated with 196 diseases to the
cell types they affect. We conduct text-mining of the PubMed database to produce an independent resource of
disease-associated cell types, which we use to validate our method.
Results: The GSC method successfully identifies known disease–cell-type associations, as well as highlighting
associations that warrant further study. This includes mast cells and multiple sclerosis, a cell population currently
being targeted in a multiple sclerosis phase 2 clinical trial. Furthermore, we build a cell-type-based diseasome using
the cell types identified as manifesting each disease, offering insight into diseases linked through etiology.
Conclusions: The data set produced in this study represents the first large-scale mapping of diseases to the cell types
in which they are manifested and will therefore be useful in the study of disease systems. Overall, we demonstrate that
our approach links disease-associated genes to the phenotypes they produce, a key goal within systems medicine.
Background
Identifying the cell types that contribute to the development of a disease is key in understanding its etiology. It
is estimated that there are at least 400 different cell types
present within the human body [1], each performing a
unique repertoire of functions, the disruption of which
may lead to the development of a disease [2]. Thousands
of genes that influence human disease have been identified through linkage analysis, genome-wide association
studies and genome sequencing [3]. In many cases, the cell
types that these genes directly affect and through which
promote disease development have yet to be characterized
or are still being debated. Identification of these cell types
*Correspondence: [email protected]
Department of Life Sciences, Imperial College London, Exhibition Road, SW7
2AZ London, UK
will further our understanding of the genetic basis of
these diseases and the underpinning molecular pathways
and processes. In this study, we refer to the cell types
directly affected by the disease-associated genes as the
disease-manifesting cell types.
Large-scale mappings have previously identified associations between diseases [4], genes [5] and tissues [6].
However, there currently exists no large-scale mapping of
diseases to the cell types in which they are manifested.
Developments in gene expression profiling technology
have led to the availability of tissue- and cell-type-specific
gene expression data [7–9], which have been integrated
with known disease-associated genes to identify systematically associations between diseases, tissues [10] and
a limited number of cell types [11]. However, a lack of
high-quality cell-type-specific gene expression data has
© 2015 Cornish et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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Cornish et al. Genome Medicine (2015) 7:95
previously limited the large-scale mapping of diseases to
cell types.
The molecular basis of diseases can also be explored
using the interactome, a network created by integrating
all interactions known to occur between proteins. Tens
of thousands of protein–protein interactions (PPIs) have
been identified [12] and used in tasks such as the prioritization of disease-associated genes [13, 14] and the
prediction of the phenotypic impact of single amino acid
variants [15]. However, the majority of methods that
detect PPIs operate in vitro, meaning that unlike gene
expression, we have little understanding of the contexts
in which PPIs take place. This lack of context-specific
PPI data means that the majority of methods that use the
interactome to explore the molecular basis of a disease
use a generic PPI network [13, 14], rather than a PPI network specific to the context of the disease being studied.
This has been seen to limit the success of these methods [16]. Computational approaches have been developed
to create context-specific biological networks [16–21].
These approaches often use gene expression data to modify generic PPI networks, either through the removal of
proteins not expressed in a given context [16–18, 20] or
through the re-weighting of interactions deemed more
likely to occur in a given context [16]. Whilst these
methods have been used to create tissue-specific interactomes, few cell-type-specific interactomes have been
created.
In this study, we integrate high-quality cell-type-specific
gene expression data and PPI data to build a collection of 73 cell-type-specific interactomes and use these
interactomes to create the first large-scale mapping of diseases to cell types. We use gene expression data from
the FANTOM5 project [8], which represents the largest
atlas of cell-type-specific gene expression produced to
date. These data were created using primary cell samples
rather than immortalized cell lines, resulting in higherquality gene expression profiles [8]. By comparing the
clustering of sets of disease-associated genes across these
cell-type-specific interactomes, we demonstrate that it is
possible to use cell-type-specific interactomes to identify the cell types in which a disease is most likely to be
manifested. This approach is validated using text-mined
disease–cell-type associations from the PubMed database.
An implementation of the method described in this study
and the 73 cell-type-specific interactomes are available
to download [22, 23]. These resources will be useful
in the identification of additional disease-associated cell
types as more gene expression data become available,
as well as in the development of tools better able to
explore the etiology of a disease given its cellular context. Using this method, we identify known disease–
cell-type associations and associations that warrant
further study.
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Methods
Method implementation and data availability
We have developed an R package called DiseaseCellTypes
that contains implementations of the gene set compactness (GSC) and gene set overexpression (GSO)
methods and the method used to create the cell-typespecific interactomes. DiseaseCellTypes is available to
download from [22]. This package also includes the
data and a vignette containing the code required to
reproduce the results detailed in this study. The 73
cell-type-specific interactomes are available to download
from [23].
The DisGeNET database
Sets of disease-associated genes were obtained from the
DisGeNET database (v2.1) [3], which integrates expertly
curated associations, associations predicted using animal
models and associations identified using automated textmining (Additional file 1: Table S1). In total, DisGeNET
contains 13,185 diseases associated with one or more
genes. We completed a number of filtering steps to extract
the highest-quality associations between human diseases
and genes, reducing the number of diseases associated
with one or more genes to 1544.
We first removed associations predicted using animal models, associations identified using automated textmining and associations related to the genetic response
to environmental chemicals, as we deemed these associations less likely to be of high quality and less relevant
to our analyses (reducing the number of diseases associated with one or more genes to 3856). We next removed
diseases classified as congenital, hereditary, and neonatal
diseases and abnormalities (due to the lack of fetal data
within the gene expression data set) and neoplasms (due
to the previous observation that unlike other diseases,
cancer-associated genes tend not to be overexpressed in
the tissues in which the cancers are located [6]) using
the disease classifications provided by DisGeNET (reducing the number of diseases to 2143). We then removed
associations extracted from the literature supported only
by a single evidence source (reducing the number of
diseases to 1898). To increase the number of genes associated with each disease, we pooled the genes associated
with sub-types of diseases wherever possible, by removing the number that follows the disease name in many
DisGeNET entries (reducing the number of diseases to
1679). We also removed genes for which there were no
gene expression data in the expression data set (reducing the number of diseases to 1557). Finally, we removed
diseases that did not map to a disease medical subject
heading (MeSH) term (from trees C and F03 of the 2015
MeSH tree structure, reducing the number of diseases to
1544). All genes were mapped to Ensembl gene identifiers
using the biomaRt R package [24].
Cornish et al. Genome Medicine (2015) 7:95
The GSC method works by measuring network distances between pairs of genes and therefore cannot be
applied to diseases with only a single associated gene. For
this reason, it is not possible to apply the GSC method
to all of the 1544 diseases with one or more associated
genes. We have demonstrated that the GSC method works
well when applied to diseases with six or more associated genes (see ‘Parameter selection’ below). Of the 1544
diseases with one or more associated genes, 196 diseases
have six or more associated genes. In the main analyses,
we therefore used this subset of 196 diseases.
The STRING database
PPI data were obtained from the STRING database (v9.1)
[12]. The STRING database integrates experimentally verified PPIs with additional data sources, including genomic
context, gene coexpression data and text-mined data.
These data are used to produce confidence scores for
the interactions. We included only experimentally verified
PPIs with a confidence score greater than 0.8 within the
cell-type-specific interactomes (see ‘Parameter selection’
below for justification). We mapped each Homo sapiens
protein identifier to an Ensembl gene identifier [24]. The
cell-type-specific interactomes each contain 32,275 interactions between 7332 proteins.
Gene expression data
The FANTOM Consortium performed cap analysis of
gene expression (CAGE) using single-molecule cDNA
sequencing to identify transcription start sites (TSSs) and
quantify their expression in H. sapiens and Mus musculus
primary cell, tissue and cell line samples [8]. In our analyses, we use only the 362 H. sapiens primary cell samples
organized into facets by Andersson et al. [9]. Individual
TSSs were identified by the FANTOM Consortium using
decomposition peak identification [8].
We downloaded the annotated CAGE peak counts
mapped to hg19 from the FANTOM5 website. Peaks
located within 500 bp of the 5 end of a gene transcript
were assigned to that gene. Gene names were mapped to
Ensembl gene identifiers [24]. Those peaks not assigned
gene transcripts or for which no Ensembl gene identifier could be found were removed. For each gene, we
summed the counts of each assigned peak to produce a
single gene-wise expression value, as described in Sardar
et al. [25]. These counts were normalized using the relative log expression method implemented within the edgeR
R package [26] to produce values representing gene-wise
tags per million.
We grouped FANTOM5 samples representing the same
primary cell-type population using the sample names.
Andersson et al. further organized these groups into
broader facets, based upon cell function and morphology
[9]. We therefore refer to the finer sample name-based
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groups as sub-facets. This produces a two-level hierarchy
of sample groupings containing the facets and sub-facets
(Additional file 2: Table S2).
Some of the facets defined by Andersson et al. [9] contain cell samples of different potency. For example, the
mesenchymal cell facet contains both somatic amniotic
membrane cells and pluripotent mesenchymal stem cells.
It has previously been demonstrated that gene expression changes with cell differentiation [27]. Therefore, we
split the mesenchymal cell facet into three facets (mesenchymal somatic cell, mesenchymal precursor cell and
mesenchymal stem cell) and the monocyte facet into two
facets (monocyte and cd14+ monocyte derived endothelial progenitor cell) and assigned sub-facets based upon
the potency of the samples.
We conducted quality control to remove spurious samples. This was done by comparing samples corresponding
to the same cell-type population and discarding samples with expression profiles that differ strongly from the
other samples. First, the sub-facets containing only one
sample were removed. Next, samples were normalized
so that their gene-wise expression values summed to 1.
The Jensen–Shannon distance (JSD) between each sample
in each sub-facet was then computed [28], as described
by Andersson et al. [9]. The JSD provides a measure of
expression profile similarity. Complete linkage agglomerative hierarchical clustering was run using the JSD to
cluster the samples. The resulting tree was cut at a height
of 0.35 to split the samples into discrete clusters within
each sub-facet. For each sub-facet, samples not contained
within the largest cluster were removed. If no cluster contained more than one sample, all samples mapped to the
sub-facet were removed. In total, 331 (91.4 %) of the 362
primary cell samples passed this quality control procedure. The 31 samples that did not pass this procedure were
discarded and not used in the later analyses.
Many of the 331 samples that passed quality control
correspond to the same cell type. The analyses conducted in this study require a single expression profile
for each cell type and it was therefore necessary to combine these replicate samples. As previously explained,
each sub-facet contains samples corresponding to a single
cell type and these sub-facets are further organized into
broader facets. However, the cell types found throughout
the body are not represented equally within these facets
and sub-facets. Cell types that are less accessible or more
difficult to isolate from primary tissues, such as pancreatic cells and dendritic cells, are less well represented. This
can be seen by comparing the number of sub-facets contained within each facet: the dendritic cell facet contains
three sub-facets, while the vascular-associated smooth
muscle cell facet contains ten sub-facets. If we were
to combine samples using the sub-facet groupings then
we would produce a disproportionately large number of
Cornish et al. Genome Medicine (2015) 7:95
vascular-associated smooth muscle cell expression profiles, which would potentially have a deleterious effect on
the later analyses. Many of the sub-facets contained within
these larger facets may represent similar cell types and
therefore contain samples with similar gene expression
profiles. Therefore, to produce a single expression profile
for each cell type whilst avoiding the production of similar
expression profiles as a result of uneven cell-type representation, we considered the similarity of the expression
profiles of the samples within each facet.
The JSD was used to measure the similarity of the
expression profiles of the samples in each facet. If the
mean JSD was greater than 0.25, we combined samples
by their sub-facet, producing multiple expression profiles
under the names of the sub-facets. If the mean JSD was
less than 0.25, we combined samples by their facet, producing a single expression profile under the name of the
facet. A value of 0.25 was chosen after manual inspection
of the functional-similarity of the sub-facets contained
within each facet, along with the mean JSD between the
samples within each facet. As previously mentioned, the
vascular-associated smooth muscle cell facet contains ten
sub-facets, many of which are functionally similar. Conversely, the mesenchymal stem cell facet contains seven
sub-facets, representing stem cell populations that will
form diverse cell and tissue types, including adipose tissue, hepatic cells and osteoblasts. We speculated that
disruption to these diverse cell and tissue types may produce different phenotypes and therefore chose a value
that would produce distinct expression profiles for each of
these sub-facets. The mean JSD between the mesenchymal stem cell facet samples is 0.259 and we therefore chose
a value of 0.25 to produce distinct expression profiles for
the sub-facets within this facet, whilst producing a single
expression profile for those facets containing functionally similar sub-facets, including the vascular-associated
smooth muscle cell facet. The gene-wise expression values
from different samples were combined by computing the
mean tags per million value. Using this procedure, expression profiles for 74 cell types were derived from the 331
samples.
Many cell types contain a small number of highly
expressed genes. For example, in reticulocytes, the expression of HBB is 176,857 times higher than the median
expression value of the gene across all cell types.
Using these raw expression values to construct contextspecific interactomes would produce interactomes containing a small number of very high-weight edges.
This would prohibit the use of the random walk with
restart (RWR) method to measure distances between
vertex pairs as these high-weight edges would come to
dominate the movement of the walker. Therefore, we
percentile-normalized the gene-wise expression values,
which ensures that the scores range between 0 and 1. This
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is similar to the approach taken by Hu et al. [11]. For each
gene in each cell type, we divided the expression value by
the mean expression value of that gene across all cell types:
kei,l
ei,l = k
j=1 ei,j
(1)
where ei,l is the expression value of gene i in cell type l,
ei,l is the relative expression value and k is the number
of cell types. For each cell type, these relative expression
values were then transformed into percentile scores x,
distributed uniformly between 0 and 1. A score of 1 indicates that a gene is the most overexpressed gene in that
cell type, while a score of 0 indicates that it is the most
underexpressed.
The gene expression profile of hepatocytes differed
from the expression profiles of the other cell types. After
division by the mean gene expression value across all cells
types (Eq. 1), 364 hepatocyte genes had a relative expression value greater than 57 (within the top 0.1 % of values).
The next highest cell type was mesenchymal somatic cells
with 71 and the median number across all cell types
was six. The application of alternative sample normalization methods, including the upper quartile and trimmed
mean of M-value methods [26], failed to resolve this. We
therefore did not use the hepatocyte expression profile in
our analyses. This reduced the total number of cell-type
profiles from 74 to 73.
Creating context-specific interactomes
We created 73 cell-type-specific interactomes using an
approach based on the edge re-weight method of Magger
et al. [16]. Each edge in graph G is assigned a weight
depending on the expression score of the interacting
genes. Let G = (V , E), where V is a set of n vertices and
E ⊆ V × V is a set of m undirected edges between pairs of
vertices. wi,j,l = xi,l × xj,l , where wi,j,l is the weight of the
edge connecting vertex i and vertex j in the network created using the gene expression data from cell type l and
xi,l is the percentile-normalized expression score of gene
i in cell type l. Larger values of w indicate a greater likelihood that an interaction takes place. Unlike the method
of Magger et al. [16], we applied no cutoff to the gene
expression data.
Gene set overexpression
The percentile-normalized gene expression scores were
used to quantify the significance of overexpression. Let S
be the set of genes associated with a disease. The significance of the overexpression of gene set S in cell type l was
measured using a permutation-based approach. To create
u permuted expression profiles, we randomly reassigned,
for each gene, the expression scores and the cell type. The
mean expression score of S was then computed in the
Cornish et al. Genome Medicine (2015) 7:95
observed and permuted expression profiles. An empirical
P value was produced by computing the proportion of permuted expression profiles in which the mean expression
score was greater than the mean expression score in the
observed profile. A minimum P value of 1/u was applied
to ensure that no P values equaled 0. We used 10,000
permutations throughout our analyses. This is the GSO
method.
Gene set compactness
The compactness score provides a measure of how
strongly a set of vertices interact in a graph. The compactness score of vertex set S on graph G is the mean
distance between pairs of vertices in S on G [29], where |S|
is the size of S and dG (i, j) is the distance between vertex
i and vertex j in graph G. We used a ranked version of the
RWR method [13] to measure distance. For each disease,
u permuted interactomes were created using expression
profiles permuted using the same approach as in the GSO
method. The compactness score was computed for each of
the observed and permuted interactomes. We define the
compactness score (C) as:
G
i,j∈S d (i, j)
(2)
C(S, G) =
|S|2
Empirical P values were produced by computing the
proportion of permuted interactomes in which the compactness score of S was smaller than the compactness
score of S in the observed interactome. A minimum
P value of 1/u was applied to ensure that no P values
equaled 0. We used 10,000 permutations throughout the
analyses. This is the GSC method.
This use of the compactness score differs from previous
uses. In Cornish et al. [30], the compactness score is used
to measure the significance of gene set clustering in a single network, through the permutation of genes in the gene
set. In the GSC method described here, the compactness
score is used to compare gene set clustering across multiple networks, through the permutation of the data used to
create the networks.
Computing network distances using the random walk with
restart method
The RWR method measures distances between vertex
pairs in a graph. Unlike simpler methods, such as the
shortest paths method, the RWR method incorporates
the entire structure of the graph when measuring distances. It has been shown to be more effective than
the shortest paths method in tasks such as disease gene
prioritization [13].
To measure the distance from vertex i to vertex j, a random walker is started from vertex i. At each time step, the
walker can either move to a vertex directly connected to
its current vertex, or move back to its starting vertex with
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restart probability r. In an unweighted graph, the probability that the walker moves to each connected vertex is
uniform. In a weighted graph, the probability distribution
is based upon the weights of the edges, so that the walker
is more likely to travel along an edge of high weight. As the
number of time steps increases, the probability that the
random walker will be located at each vertex converges to
a steady state [31].
We used a method based on the iterative approach
described by Köhler et al. [13] to compute the RWR distances. Let A be the column-normalized adjacency matrix
of graph G, using edge weights w. p is a probability matrix
with dimensions equal to n, the number of vertices in G.
The element pti,j is the probability that a walker starting
from vertex i is located at vertex j at time t. The initial
probability matrix p0 is an identity matrix. Probabilities
can be computed iteratively using:
pt+1 = (1 − r)Apt + rp0
(3)
Iterations are conducted until the change in the probability matrix across time steps (pt and pt+1 , measured
using the Manhattan distance) falls below a cutoff. To
save computational time, we computed only the distances
between the vertices in the vertex set S and all of the vertices in the graph. A restart probability r of 0.7 was used
(see ‘Parameter selection’ below for justification), along
with an iteration cutoff of |S|×10−5 . For each vertex i, vertices are ranked by their probability, so that the vertex that
the random walker is most likely to be located on is ranked
first. These ranks are used as the distances between the
vertices in Eq. 2.
Text-mining of disease–cell-type associations
Text-mining of the PubMed database was also used to
identify disease-associated cell types [32]. This was done
by first mapping each cell type and disease to one or more
MeSH terms. These MeSH terms were then used to query
the PubMed database and identify diseases and cell types
that were co-mentioned in articles more frequently than
expected by chance.
We mapped every FANTOM5 project facet and subfacet to one or more MeSH terms. For many facets
and sub-facets, no single MeSH term from the MeSH
Cells tree (A11) contained enough anatomical information to differentiate it from the other facets and sub-facets.
Therefore, we mapped some facets and sub-facets to two
MeSH terms: one representing the cell type from the
MeSH Cells tree (A11) and one containing additional
anatomical information from an alternative anatomical MeSH tree. In these cases, we used the overlap of
the results for each term when querying the PubMed
database.
The FANTOM Consortium provides an ontology (FF)
to which they map each facet and sub-facet [8]. This
Cornish et al. Genome Medicine (2015) 7:95
ontology contains cross-mapping to the cell ontology (CL)
[1]. Using these ontologies, each facet and sub-facet was
mapped to a CL term. If the FF term of a facet or sub-facet
was not cross-mapped to a CL term, then its ancestral
FF terms in the FF ontology were considered. If multiple ancestors or CL terms were found, or if no CL
term was found, the most representative CL term was
selected manually. For each CL term, MeSH terms from
the MeSH Cells tree (A11) were obtained by querying the
MeSH database. If a MeSH term did not contain sufficient anatomical information to differentiate the facet or
sub-facet from others, then we used the Uberon anatomical ontology [33], which is also cross-referenced in FF.
Anatomical MeSH terms were obtained by querying the
MeSH database with the Uberon term when not available
in the Uberon ontology.
Each disease was mapped to a MeSH term through
United Medical Language System (UMLS) terms [34].
Each disease within the DisGeNET database is associated
with a UMLS term. We used the UMLS Metathesaurus
to map each UMLS term to a disease MeSH term (from
trees C and F03). Diseases associated with UMLS terms
not present in the UMLS Metathesaurus were mapped to
MeSH terms manually by querying the MeSH database
with the UMLS term.
While we have attempted to map each cell type and disease to a unique MeSH term or pair of MeSH terms, the
lack of specific terms in some areas of the MeSH database
prevented us from doing this. Therefore, some diseases
and cell types are mapped to the same terms. Also, due to
the ontological structure of the MeSH database, some of
the MeSH terms mapped to facets, sub-facets and diseases
are either the ancestors or offspring of other MeSH terms.
We do not consider these relationships when comparing
the text-mined associations to the associations produced
using the GSC and GSO methods.
Fisher’s exact test was used to measure the significance
of observing the number of articles co-mentioning terms
given the number of articles mentioning the terms individually [35]. P values were obtained from a contingency
table containing the number of co-occurrences of the cell
type and disease, the cell type without the disease, the disease without the cell type and the remaining number of
articles within the corpus (the corpus is the total number of articles within the PubMed database). PubMed was
queried using the Entrez Programming Utilities (eUtils)
[36] on 23 April 2015.
Parameter selection
As previously described, cell-type-specific interactomes
were created using physical interactions from the
STRING database with confidence scores greater than 0.8.
Distances were measured across these interactomes using
a restart probability r. We measured the effect of these
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two parameters on the GSC method by applying the GSC
method to cell-type-specific interactomes created using
confidence cutoff scores of 0.0, 0.2, 0.4, 0.6 and 0.8 and
using restart probabilities of 0.1, 0.3, 0.5, 0.7 and 0.9.
Diseases from the DisGeNET data set with at least two
associated genes were used. Performance was measured
by comparing the disease–cell-type associations identified
using the GSC method to the associations identified using
text-mining. By considering the text-mined associations
as true positives, we are able to estimate the precision,
recall and F1 score for the GSC method. As previously
explained, text-mining is likely to identify a number of
false positives and therefore these performance scores are
likely to be under-estimated. However, they provide us
with a method of comparing GSC method performance
across the parameter space.
Applying the GSC method to interactomes created
using different confidence score cutoffs had little effect
on method performance (Additional file 3: Table S3). We
therefore applied a confidence score cutoff of 0.8 to reduce
the density of the network.
Using restart probabilities between 0.1 and 0.9 had little effect on method performance (Additional file 3: Table
S3). A previous method that used the RWR method to
identify gene–phenotype relationships successfully used a
restart probability of 0.7 [37]. For these reasons, we ran
the GSC method with a restart probability of 0.7.
To identify the number of disease-associated genes
required by the GSC method, we applied the GSC method
to sets of diseases with similar numbers of associated
genes (Additional file 4: Table S4). Each disease set contains at least 20 diseases. To create the disease sets,
diseases were first sorted by their respective number of
associated genes. Diseases were added to the disease sets
in this sorted order, starting from the disease with the
greatest number of associated genes. Diseases were added
to the same disease set until the number of diseases
in the set reached 20. When this occurred, a new disease set was created and the disease added to this new
disease set. Diseases with the same number of associated genes were always added to the same disease set.
Cell-type-specific interactomes were created using a confidence cutoff score of 0.8 and random walks completed
using a restart probability of 0.7. Text-mined disease–celltype associations were used to measure the performance
of the GSC method on each disease set. As previously
explained, the GSC method cannot be applied to diseases with only a single associated gene, as the method
works by measuring the network distances between pairs
of genes. The overlap between the GSC and text-mined
associations is significant for diseases with three or more
associated genes. However, there is a large improvement
in GSC method performance when the number of associated genes increases from five to six. For this reason, we
Cornish et al. Genome Medicine (2015) 7:95
completed the analyses using the 196 diseases with six or
more associated genes.
Constructing a diseasome using causal cell types
We produced a diseasome by connecting each disease to
the four diseases with which it correlated most strongly
with respect to the cell types identified as associated
by the GSC method. For each disease pair, Pearson’s
product-moment correlation coefficient was computed
using the − log10 of the P values corrected using the
Benjamini–Hochberg procedure for multiple testing [38].
We removed diseases with no cell-type associations passing a false discovery rate (FDR) of 10 %. We chose to
connect each disease to a fixed number of other diseases,
rather than applying a correlation cutoff, as this allows
for the identification of the strongest correlations for each
disease. Adding edges between all disease pairs that pass
a correlation cutoff produces a diseasome in which some
diseases have a large degree, while other diseases have no
connections. A value of four was chosen as it produced a
diseasome in which clusters of diseases of the same class
could easily be identified. We have also created diseasomes by connecting each disease to the two (Additional
file 5: Figure S1), three (Additional file 6: Figure S2) and
five (Additional file 7: Figure S3) diseases with which they
correlate most strongly. Clustering of diseases of the same
class can also be seen in these alternative diseasomes.
Vertices in the diseasome are colored by the class of the
disease. These disease classes were obtained from MeSH.
The mapping of DisGeNET diseases to MeSH terms
employed in the text-mining was used to map the diseases to the MeSH ontology. For each disease, ancestors
at the second level of the MeSH ontology were identified.
Many diseases have multiple ancestors at this level. When
diseases mapped to multiple ancestral terms, we chose
the ancestral terms occurring most frequently across all
diseases to represent each disease, as this reduced the
number of classes represented in the network, making
the disease clusters easier to identify. Diseases belonging
to classes represented in the diseasome fewer than eight
times were combined within the other class. Edges connecting two diseases of the same disease class are also
colored using the color of the disease class. Vertices in
the diseasome are arranged using the Fruchterman and
Reingold layout algorithm implemented in the igraph R
package [39] with the default parameters.
Enrichment of high-weight edges in the monocyte-specific
psoriasis sub-network
The monocyte-specific psoriasis sub-network was created
by identifying the protein products of genes associated
with psoriasis and their interacting partners. In Fig. 1,
proteins with more than 15 interacting partners were
removed to improve visual interpretation. To determine
Page 7 of 18
whether the monocyte-specific psoriasis sub-network
is enriched with high-weight edges, 10,000 permuted
sub-networks were created by permuting the monocyte
percentile-normalized gene expression scores. The number of edges with a weight greater than 0.90 (within the
top 1 % of edge weights in the monocyte-specific interactome) were counted for the observed sub-network and
each of the permuted sub-networks. Only 1/10,000 of the
permuted sub-networks contained a greater number of
high-weights edges than the observed sub-network, producing an empirical P value of 0.0001. This enrichment
analysis was completed without removing proteins with
more than 15 interacting partners.
Results
Using gene set compactness to identify
disease-manifesting cell types
The FANTOM5 project used CAGE in different cell types
to identify TSSs and quantify their expression [8, 9].
We combined the TSS-wise expression values to produce
gene-wise percentile-normalized [25] relative expression
scores (see ‘Methods’). In total, gene expression profiles
were produced for 73 cell types.
Disease-associated genes are often enriched within certain pathways [2], the disruption of which leads to the
disease. Cellular pathways are represented within PPI
networks and because of this, sets of disease-associated
genes tend to cluster within PPI networks [4, 40]. This
is exemplified by the results produced by gene prioritization tools such as PRINCE [14], which use the clustering of disease-associated genes within PPI networks
to prioritize candidate genes. Pathways whose disruption
leads to a disease are likely to be active within the cell
types associated with the disease. Therefore, we would
expect disease-associated genes to cluster most strongly
in the interactomes specific to the disease-manifesting cell
types, providing us with a method of identifying these cell
types. As no cell-type-specific interactomes are available,
we integrated the FANTOM5 project cell-type-specific
gene expression data with 32,275 PPIs from the STRING
database [12] to build the largest collection of cell-typespecific interactomes currently available. In these interactomes, each vertex represents a gene and each edge
a physical interaction weighted using the product of the
gene pair’s percentile-normalized gene expression scores
(Additional file 8: Figure S4).
We introduce the compactness score [29] to identify
the cell-type-specific interactomes within which sets of
disease-associated genes are significantly more clustered
than expected by chance (Fig. 2A) and thereby identify
disease-manifesting cell types. The compactness score is
defined as the mean distance between pairs of vertices
in a set in a graph. The smaller the compactness score
of a vertex set, the stronger the interactions between the
Cornish et al. Genome Medicine (2015) 7:95
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Fig. 1 Monocyte-specific psoriasis sub-network. To create the sub-network, the protein products of all psoriasis-associated genes (squares) and their
interacting partners (circles) were identified. To improve visual interpretation, psoriasis-associated genes with more than 15 interacting partners
were removed. The more overexpressed each gene is in monocytes, the darker the color of the vertex. Edges with larger weights are darker blue
and thicker. This sub-network is enriched with high-weight edges (P = 0.0001 when genes with more than 15 interacting partners are not removed)
that connect many of the disease-associated genes, suggesting that cell-type-specific interactomes may be useful in gene prioritization
vertices in the set. If a vertex set interacts more strongly
than expected by chance, then the vertex set can be said
to cluster [30].
To quantify clustering significance, we permute the
expression profiles and use these permuted profiles to
build permuted interactomes (see ‘Methods’). A P value
is produced for each cell type by comparing the compactness score of the vertex set on the observed and
permuted cell-type-specific interactomes. In total, this
method identified 660 associations between 73 cell types
from the FANTOM5 project data and 196 diseases from
the DisGeNET database of disease–gene associations [3]
(Fig. 3, Additional file 9: Figure S5, Additional file 10:
Table S5, Additional file 11: Table S6, at an FDR of 10
% computed using the Benjamini–Hochberg procedure
for multiple testing [38]). This set of 196 diseases contains the diseases in the DisGeNET database associated
with six or more genes, after the application of multiple
filtering steps used to select the highest-quality associations between human diseases and genes. This is the GSC
method.
Using text-mining to identify disease-manifesting cell
types
To provide an assessment of the validity of the above
approach, we used text-mining to produce an independent
literature-based association of diseases to cell types. Textmining has previously been used to identify associations
between diseases and various biological entities, including
genes [5] and tissues [6]. However, to our knowledge,
no study has used this approach to identify associations
between diseases and a large number of cell types, possibly
due to the difficulties in identifying articles that mention
highly specific cell types.
We used the PubMed database of articles to identify
associations between cell types and diseases (Fig. 2B).
Articles within the PubMed database are annotated with
MeSH terms, a controlled vocabulary that describes the
topics of each article [36]. Whether two MeSH terms are
associated or not can be determined by counting the number of articles that mention each term individually and
comparing this to the number of articles that mention
both terms. If the number of co-mentions is greater than
expected given the number of individual mentions, then
the terms can be said to be associated [32].
To conduct the text-mining, it was first necessary
to map the cell types and diseases to MeSH terms
(Additional file 12: Table S7 and Additional file 13:
Table S8). This was done using the cross-referencing
provided by the FANTOM Consortium and DisGeNET,
a number of controlled vocabularies, including UMLS
[34] and Uberon [33], the MeSH database and manual curation. Mapping diseases was relatively simple,
as there exists a unique MeSH term for the majority of diseases in DisGeNET. Mapping cell types was
more difficult, due to the relatively small number
of cell types represented in the MeSH database. For
example, the 73 cell types include five different types
Cornish et al. Genome Medicine (2015) 7:95
Page 9 of 18
A
B
C
Fig. 2 Overview of the GSC, text-mining and GSO methods. (A) For the GSC method, percentile-normalized relative gene expression scores are
integrated with PPI data to create interactomes. Permuted interactomes are created by permuting the expression scores. The compactness score of
the disease-associated gene set is computed for each observed and permuted interactome and empirical P values produced by counting the
proportion of permuted compactness scores less than the observed compactness score. (B) To complete the text-mining, diseases from DisGeNET
and cell types from the FANTOM5 project were mapped to MeSH terms using a number of controlled vocabularies. These MeSH terms were then
used to query PubMed and count the number of articles individually and co-mentioning terms. Fisher’s exact test was used to determine whether
the number of co-mentioning articles is greater than expected by chance. (C) For the GSO method, percentile-normalized gene expression scores
are used to create observed and permuted expression profiles. The mean expression score of the disease-associated gene set is then computed for
the observed and permuted expression profiles. Empirical P values are computed by counting the numbers of permuted scores greater that each
observed score
of fibroblast (fibroblasts of the choroid plexus, gingiva,
lymphatic vessel, periodontium and tunica adventitia
of artery). The MeSH database does not contain a
unique term for each of these fibroblast sub-types.
We therefore combined the most-representative cellular MeSH term (in this example ‘Fibroblast’) with
anatomical MeSH terms to differentiate between the cell
types. These combined MeSH terms were then used
to query PubMed. A one-tailed Fisher’s exact test was
used to identify cell-type/disease pairs co-mentioned
more frequently than expected by chance [35]. In total,
text-mining identified 1150 associations between the
73 cell types and the 196 diseases (Additional file 14:
Table S9).
A disadvantage of this method is that it does not take
into account the context in which a cell type or disease
is mentioned in an article. This prevents us from distinguishing between those cell types directly affected by the
disease-associated genes and those cell types indirectly
affected later in the development of the disease. Psoriasis is a chronic skin condition generally considered
to be caused by environmental and genetic factors and
Cornish et al. Genome Medicine (2015) 7:95
Page 10 of 18
Fig. 3 Heat map of a subset of the disease–cell-type associations identified by the GSC method. The darker the shade of green, the stronger the
association. P values have been corrected using the Benjamini–Hochberg procedure for multiple testing. Each cell type and disease is involved in at
least two associations with q < 0.1. Cell types and diseases have been clustered using complete-linkage hierarchical clustering. Additional file 9:
Figure S5 contains the complete set of associations identified. MDEPC: monocyte-derived endothelial progenitor cell, MLNS: mucocutaneous lymph
node syndrome, MID: monocyte immature derived, PDCO: pulmonary disease, chronic obstructive, PIDC: primary idiopathic dilated
cardiomyopathy, SARS: severe acute respiratory syndrome, SSP: subacute sclerosing panencephalitis
Cornish et al. Genome Medicine (2015) 7:95
Page 11 of 18
disruption to the immune system [41]. Skin cell types
are strongly affected by psoriasis and they are therefore
often mentioned in articles referring to the disease. While
our text-mining identifies skin cells as being strongly
associated with the disease (epidermal keratinocytes:
q = 10−319 , the q value is analogous to the P value and
represents the minimum FDR at which the association can
be declared significant), it is unable to determine whether
the disease-associated genes disrupt processes and pathways active within the skin cells or whether the skin cells
are indirectly affected.
We used the text-mined disease–cell-type-associations
to measure the performance of the GSC method when
applied to diseases with different numbers of associated
genes (Additional file 4: Table S4). Sets of disease–celltype associations were created by applying an FDR cutoff
of 10 % to the results. As the number of genes associated with each disease increases, the overlap between the
GSC and text-mined associations also increases, indicating that the GSC method performs better on diseases
with a greater number of associated genes. The overlap
between the GSC and text-mined associations is significant for diseases with three or more associated genes.
All of these associations are contained within the additional files (Additional file 10: Table S5 and Additional
file 14: Table S9). There is a large increase in the significance of the overlap when the number of associated
genes increases to six. For this reason, we decided to
complete the remaining analyses using the 196 diseases in
the DisGeNET data set associated with six or more genes.
Comparison of disease-manifesting cell-type identification
methods
Alongside the GSC and text-mining methods, we used the
overexpression of sets of disease-associated genes to identify associated cell types (Fig. 2C, Additional file 15: Table
S10 and Additional file 16: Table S11, see ‘Methods’), an
approach previously found to be successful [11]. This is
the GSO method.
Although the GSC and text-mining methods identify
different associations (Additional file 17: Figure S6 and
Additional file 18: Figure S7), the overlap is significant at
FDRs of 5, 10 and 20 % (Table 1 and Additional file 19:
Table 1 Number of disease–cell-type associations identified by
the GSC and GSO methods supported by text-mining at a 10 %
FDR. Text-mining overlap significance was computed using
Fisher’s exact test
Method
GSC
GSO
Supported by text-mining
320
269
Not supported by text-mining
340
294
Text-mining overlap significance
5.80 × 10−183
1.57 × 10−149
Table S12), demonstrating that a significant proportion
of the associations identified by the method are supported by published literature, providing validation for
the method. At these FDRs, the overlap between the
GSO and text-mining results is also significant. While a
similar proportion of GSC- and GSO-identified associations are supported by text-mining (Additional file 20:
Figure S8), the advantage of using cell-type-specific interactomes to explore the molecular mechanisms that drive
disease is their ability to identify previously unidentified disease-associated genes and pathways. These results
demonstrate that disease-associated genes interact more
strongly in the interactomes of the disease-manifesting
cell types. These interactomes are therefore more likely to
be informative when identifying previously unidentified
disease-associated genes.
The GSC and GSO methods identify different sets of
associated cell types, indicating that they represent complementary approaches. However, there is still a large
amount of support between the GSC and GSO methods (Additional file 21: Figure S9). For 64.0 % of diseases,
at least 50 % of the associations identified by the GSC
method are supported by the GSO method.
Using the MeSH term mapped to each disease and
the cell ontology term mapped to each cell type [1], it
is possible to identify classes of diseases and cell types.
The GSC, GSO and text-mining methods identify different numbers of associations between diseases and cell
types of different classes (Table 2). A greater proportion of the cell types identified by the GSC and GSO
methods as being associated with immune system diseases are cell types of the immune system. Conversely,
a greater proportion of the cell types identified by textmining as being associated with cardiovascular diseases
and mental disorders are cell types of the cardiovascular system and neural cell types, respectively. The GSC
and GSO methods identify low numbers of cardiovascular
cell types as being associated with cardiovascular diseases,
compared to text-mining. This may indicate that these
methods are less effective when applied to this disease
class. Many of the cell types identified as associated with
cardiovascular diseases by the GSC and GSO methods are
cell types of the immune system, possibly reflecting an
important role for the immune system in cardiovascular
disease development [42]. As previously mentioned, the
text-mining method used in this study does not take into
account the context in which a cell type or disease is mentioned and therefore cannot distinguish between the cell
types directly affected by the disease-associated genes and
indirectly affected cell types. This may contribute to the
differences seen between the methods. To test this, textmining methods that are able to incorporate the contexts
in which cell types and diseases are mentioned in articles
will need to be developed and applied.
Cornish et al. Genome Medicine (2015) 7:95
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Table 2 Associations between diseases and cell types of particular classes. The table shows the proportion of cell types in a class
identified as associated with a disease class
Disease class
Cell-type class
GSC
GSO
Text-mining
Cardiovascular diseases
Cardiovascular cells
4.7 % (5/107)
7.2 % (7/97)
27.3 % (50/183)
Cardiovascular diseases
Immune system cells
42.1 % (45/107)
36.1 % (35/97)
19.1 % (35/183)
Immune system diseases
Immune system cells
87.8 % (129/146)
86.5 % (109/126)
58.4 % (118/202)
Mental disorders
Neural cells
51.6 % (16/31)
38.7 % (12/31)
66.7 % (12/18)
The MeSH database and cell ontology were used to identify diseases and cell types belonging to each class. A disease and cell type were said to belong to a class if they were
descendants of the following terms: cardiovascular diseases (C14), cardiovascular cells (CL:0002139 and CL:0002494), immune system diseases (C20), immune system cells
(CL:0000738), mental disorders (F03) and neural cells (CL:0002319). An FDR cutoff of 10 % was applied to the results of each method to produce sets of disease–cell-type
associations
Examples of disease-manifesting cell types identified by
gene set compactness
The GSC method identifies well-characterized associations between diseases and cell types, as well as
associations that warrant further study. The integration
of multiple data sources, such as cell-type-specific gene
expression data, PPI data and disease-associated genes,
provides the key to identifying the cellular basis of disease.
A large number of associations are identified between
cell types of the immune system and autoimmune disorders, such as rheumatoid arthritis and macrophages
(q = 0.005). In addition, susceptibilities to infectious
diseases, including malaria falciparum and hepatitis B,
are associated with immune system cell types, including
monocytes and neutrophils (all q = 0.005). Neurons and
neuronal stem cells are identified as being associated with
a number of mental disorders, including psychotic disorders such as schizophrenia and bipolar disorder, and
substance abuse disorders such as tobacco use disorder
and alcoholism (all q = 0.005). The method also identifies associations between highly localized diseases and
known associated cell types, including lens epithelial cells
and retinal diseases (q = 0.005).
The GSC method identifies associations between mast
cells and a number of diseases, including asthma and multiple sclerosis (MS, both q = 0.017). While the involvement of mast cells in allergic diseases such as asthma is
well understood [43], the association between mast cells
and MS is less well characterized. Although text-mining
does not identify an association between mast cells and
MS, there is some evidence that mast cells may play a
role in the initiation and progression of the disease. Mast
cells are known to be key regulators of the permeability
of the blood–brain barrier [44] and decreased permeability of this barrier is one of the earliest signs of MS [45].
Furthermore masitinib, an inhibitor of mast cell activity,
migration and survival, was seen to produce small but
non-significant improvements in MS patients in a phase
2a clinical trial [46]. A phase 2b/3 clinical trial of masitinib
and MS is currently underway (ClinicalTrials.gov ID:
NCT01433497).
Preeclampsia is defined as the new onset of proteinuria
and hypertension during the second half of pregnancy
and affects 5–8 % of pregnancies [47]. Endothelial cells,
immune cells and adipocytes have all been implicated in
the development of preeclampsia. Endothelial cells are
essential in the remodeling of maternal vessels to provide
oxygen and nutrients to the developing fetus and placenta
[48] and aberrant formation of these endothelial cells
prevents these changes from occurring [49]. It has been
suggested that natural killer cells may contribute to this
endothelial cell dysfunction through the production of
signaling proteins that affect the migration of the endothelial cells [50]. Adipocytes produce adipokines known to
affect endothelial cell function and have therefore also
been implicated in endothelial cell dysfunction [51]. Furthermore, obesity is known to both affect the production
of these adipokines and increase the risk of preeclampsia
threefold [51]. The GSC method identifies fat cells as
the cell type most strongly associated with preeclampsia
(q
=
0.059) supporting the hypothesis that
adipocytes influence the development of the disease, possibly through the aberrant production of
adipokines.
Osteoarthritis is considered an age-related disease and
affects 14 % of people over the age of 60 [52]. While
many individuals exhibit age-related changes within their
joints, only some display the symptoms associated with
osteoarthritis [53]. Multiple genes have been identified
as being associated with osteoarthritis, but how these
genes influence the development of the disease is still
not known. Chondrocytes are the only cell type residing in the adult cartilage matrix and are responsible for
the repair of the cartilage [54]. Osteoarthritis-associated
genes may therefore influence the development of the disease through the disruption of the chondrocytes and this
repair process. Parts of the inflammatory complement
system have been observed to be present at elevated levels in the synovial fluids of individuals with early-stage
osteoarthritis [55]. Mice models of osteoarthritis genetically deficient in these complement components exhibit
less cartilage loss than mice that are not deficient in these
Cornish et al. Genome Medicine (2015) 7:95
components [55]. This demonstrates the importance of
the immune system in the development of osteoarthritis and raises the possibility that osteoarthritis-associated
genes promote the development of the disease through
the disruption of the cells of the immune system. However, the GSC method identifies chondrocytes as the
cell type most significantly associated with osteoarthritis (q = 0.005), supporting the hypothesis that chondrocyte dysregulation is key to the development of the
disease. Inflammation may occur as a result of this
dysregulation.
Page 13 of 18
ILC2s have been observed at lower frequencies in the
adipose tissues of obese humans compared to non-obese
humans [66], leading to the suggestion that disruption of
this immune system cell population may promote obesity
in humans. These results, along with the connections in
the diseasome between morbid obesity and diseases of the
immune system, provide support for involvement of the
immune system in the development and progression of
severe forms of obesity. Integration of disease-manifesting
cell-type data with additional genomic and clinical data
will likely improve our ability to identify important connections between diseases.
Cell-type-based diseasome
A diseasome represents a network of diseases, connected
by aspects of their etiology or treatment [4, 56]. It has been
demonstrated that diseases that share molecular mechanisms are more likely to exhibit clinical co-morbidity and
share drug treatments [56]. There is therefore much interest in mapping diseasomes, to aid in both disease study
and drug re-purposing. Diseasomes have been created
using shared associated genes [4], affected cellular pathways [57], co-occurrence in clinical records [58], common
symptoms [59] and through the integration of these data
[56]. We used the associated cell types identified by the
GSC method to construct a diseasome based on common
associated cell types (Fig. 4), the first of its kind.
Many diseases within the cell-type-based diseasome
interact with diseases of the same class, an attribute
shared with previously created diseasomes [4, 56]. Some
diseases also interact with diseases of a different class,
although many of these cases can be explained. For example, alopecia areata and systemic scleroderma are classified as skin and connective tissue diseases and interact
strongly with immune system diseases, reflecting their
autoimmune origin [60, 61].
The cell-type-based diseasome is also likely to be important in identifying novel associations between diseases
and providing support for previously suggested associations. In the diseasome, obesity (classified as a body mass
index greater than 30 [62]) is connected to a number
of diseases with which it has an observed co-morbidity,
including type II diabetes and hypertension [63]. However,
morbid obesity (classified as a body mass index greater
than 40 [62]) is most strongly correlated with four diseases
of the immune system (three from the immune system
diseases class and celiac disease). Increased macrophage
numbers have been observed in the adipose tissues of
both obese mice and obese humans and to contribute to
the activation of inflammatory pathways in these tissues
[64]. Group 2 innate lymphoid cells (ILC2s), a cell population involved in the regulation of adaptive immunity [65],
have been recently observed to be critically involved in the
regulation of brown and beige adipocytes [66], which are
linked to the prevention of weight gain [67]. Furthermore,
Discussion
A comprehensive understanding of genotype, gene
expression and phenotype represents one of the main
challenges of modern genetics. Studies looking at the
cis-effect on gene expression could revolutionize our
understanding of disease, but they require biological samples, which require invasive procedures such as biopsies.
Moreover, the power of a study aimed at detecting the
cis-effect on gene expression using biological samples is
greatly limited by the presence of a non-homogeneous
cell population in these tissues, such as blood, adipose or
liver tissues. We have developed a novel approach that
integrates cell-type-specific gene expression data with PPI
data to identify those cell types through which sets of
disease-associated genes exert their effect. Available for
download is an R package called DiseaseCellTypes [22],
containing implementations of the GSC and GSO methods and methods for building cell-type-specific interactomes.
It has previously been demonstrated that gene expression and PPI data can be used to identify the tissues in
which diseases are manifested [16]. However, the lack
of high-quality cell-type-specific expression data and the
absence of a systematic map between cell types and diseases have limited research into disease-manifesting cell
types. We have used text-mining to demonstrate that
by comparing the clustering of disease-associated genes
across cell-type-specific interactomes, it is possible to
identify disease-manifesting cell types. It has recently
been shown that the enrichment of disease-causing SNPs
within cell-type-specific cis-regulatory regions can also
be used to identify the cell types in which a disease is
manifested [68]. New methods will need to be developed
to integrate these and other data types to better identify
cell types that underlie disease conditions.
The interactomes created in this study represent the
largest collection of H. sapiens cell-type-specific interactomes created to date. As well as identifying diseasemanifesting cell types, these cell-type-specific interactomes are also likely to be useful in the prioritization
of disease genes and variants. Many gene-prioritization
Cornish et al. Genome Medicine (2015) 7:95
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Fig. 4 The disease-manifesting cell-type-based diseasome. Each vertex represents a disease and is colored by the disease class. Each disease is
connected to the four diseases with which it correlates most strongly (Pearson’s product-moment correlation coefficient) with respect to the
associated cell types identified by the GSC method. Edges connecting two diseases of the same class are colored with the class color. AJR: arthritis,
juvenile rheumatoid, IBD: inflammatory bowel disease, LED: lupus erythematosus, discoid, MLNS: mucocutaneous lymph node syndrome, T1D:
diabetes mellitus, type 1
tools use PPI networks to identify genes whose protein products interact with the products of known
disease-associated genes, under the hypothesis that these
genes may be involved in the same functions and pathways [13, 14]. Figure 1 shows part of the monocytespecific interactome, with genes known to be associated with psoriasis represented as squares. Many of
the psoriasis-associated genes form part of a monocytespecific sub-network enriched with high-weight edges
(P = 0.0001, see ‘Methods’). This sub-network could
be used to prioritize additional psoriasis-associated
genes. Genes that do not form part of this monocytespecific sub-network may influence the disease through
another cell type. While gene prioritization is beyond
the scope of this study, the findings here indicate that
a suitable prediction algorithm using cell-type-specific
interactomes may aid in the prioritization of candidate
genes.
While the GSC method is able to identify cell types associated with the majority of the 196 analyzed diseases, it
identifies no associated cell type for 57 diseases (FDR of 10
%). Some of these cases may be due to the lack of the true
disease-manifesting cell type within the tested expression
profiles. Easily accessible cell types, such as those found
circulating in the blood, are well represented in the FANTOM5 project primary cell samples. Cell types that are
more difficult to extract (such as pancreatic cells) are less
well represented and this prevents the identification of
some cell types associated with some diseases (such as
susceptibility to chronic pancreatitis).
All of the FANTOM5 primary cell samples represent
healthy cells (rather than cancerous cell lines). It is known
Cornish et al. Genome Medicine (2015) 7:95
that the gene expression profiles of healthy cells differ
significantly from those of diseased cells [69], representing a different set of active processes and pathways. This
lack of diseased cell expression data may therefore further limit our ability to identify disease-manifesting cell
types. As projects like FANTOM5 continue to grow,
gene expression data from more conditions, such as
disease states and developmental stages, will become
available.
The lack of a single cell type underlying the development of a disease may prevent the identification of cell
types associated with the disease. Primary ovarian insufficiency is defined as the failure of ovarian function before
the age of 40 [70]. There are a large number of causes
of this disorder, including hormonal dysfunction, autoimmunity and abnormal development of the ovaries [70].
The GSC method identifies no cell types as being associated with primary ovarian insufficiency, possibly due to
the multifactorial nature of the disorder. Furthermore, the
spread of genetic variants with small effect sizes across
multiple genes may prevent the identification of diseasemanifesting cell types.
The number of genes associated with each disease limits
the effectiveness of the GSC method. However, as genome
sequencing costs continue to decline, the number of identified disease-associated genes will increase, allowing the
GSC method to be applied to new diseases. For many of
the diseases in this analysis, the cell types directly affected
by the associated genes have yet to be identified or are
still being debated. Through the examples we provide,
we demonstrate that the GSC method is able to provide
information about these cell types.
Conclusions
The data set produced in this study represents the first
large-scale mapping of diseases to the cell types in which
they are manifested. Our method successfully identifies
many disease-associated cell types supported by previously published literature, as well as highlighting associations worthy of further investigation, including associations involving MS and preeclampsia. These associations
will be useful in many tasks within disease research, such
as prioritizing genetic variants and producing hypotheses
about disease initiation and development. Furthermore,
the cell-type-specific interactomes we have produced will
be useful in analyzing the complex etiology of disease.
As the amount of cell-type-specific data increases, tools
for identifying disease-manifesting cell types will become
increasingly important. Increased availability of gene
expression, proteomic and epigenetic data from additional cell types, developmental stages and disease states
will facilitate the fine mapping of disease-manifesting cell
types. These cell types will represent both candidates for
further study and targets for therapeutics.
Page 15 of 18
Additional files
Additional file 1: Table S1. Disease–gene associations extracted from
DisGeNET used in the analyses. DISEASE: Name of the disease in the
analysis. GENE: Ensembl gene identifier of the associated gene.
PUBMED_ID: PubMed identifiers reported by DisGeNET as supporting the
association between the gene and the disease. SNP_ID: dbSNP identifiers
reported by DisGeNET as linking the gene to the disease. (XLS 830 kb)
Additional file 2: Table S2. Mappings from each of the FANTOM5 project
primary cell-type samples to its facet, sub-facet and cell-type group.
CELL_TYPE: Name of the cell-type group that each sample was finally
assigned to. The sample expression data was combined using these groups
to produce expression profiles for 73 cell types. If the value in CELL_TYPE is
NA then the sample was discarded, either due to a lack of replicate
samples or because of the quality-control procedures. (XLS 42.5 kb)
Additional file 3: Table S3. Performance of the GSC method when
applied to interactomes created using various confidence score cutoffs
and run using various restart probabilities. All diseases with two or more
associated genes were used. Performance is measured using associations
identified by the text-mining as true positives. Sets of disease–cell-type
associations were produced by applying an FDR cutoff of 10 % to the GSC
and text-mining results. (XLS 10 kb)
Additional file 4: Table S4. Performance of the GSC method when
applied to sets of diseases with different numbers of associated genes.
Performance is measured using associations identified by the text-mining
as true positives. Sets of disease–cell-type associations were produced by
applying an FDR cutoff of 10 % to the GSC and text-mined associations.
Overlap significance was measured using Fisher’s exact test. The GSC
method cannot be applied to diseases with only one associated gene and
for this reason, the corresponding statistics are not available. (XLS 7.50 kb)
Additional file 5: Figure S1. Disease-manifesting cell-type-based
diseasome. Created by connecting each disease to the two diseases with
which it correlates most strongly with respect to the associated cell types.
(PDF 951 kb)
Additional file 6: Figure S2. Disease-manifesting cell-type-based
diseasome. Created by connecting each disease to the three diseases with
which it correlates most strongly with respect to the associated cell types.
(PDF 980 kb)
Additional file 7: Figure S3. Disease-manifesting cell-type-based
diseasome. Created by connecting each disease to the five diseases with
which it correlates most strongly with respect to the associated cell types.
(PDF 1090 kb)
Additional file 8: Figure S4. Example of the construction of a toy
cell-type-specific interactome using gene expression and PPI data.
Simulated (A) percentile-normalized gene expression scores and (B) PPI
data are used. Edge weights (w) in the re-weighted network (C) are the
product of the expression scores of the interactors. Higher weights are
indicative of stronger interactions and therefore smaller distances between
the interactors. To illustrate this, the distance along each edge is set as the
reciprocal of the weight. The thickness and color of each edge is also
proportional to the weight, with higher-weight edges represented by
thicker and darker lines. Each vertex is colored by its gene expression score,
so that vertices with higher expression scores are darker in color. From this
toy example, it is clear that re-weighting the edges of the network results
in the high-scoring vertices interacting more strongly (for example, g and
h). (PDF 679 kb )
Additional file 9: Figure S5. Heat map of disease–cell-type associations
identified by the GSC method between 73 cell types and 196 diseases. The
darker the shade of green, the stronger the association. P values have been
corrected for multiple testing using the Benjamini–Hochberg procedure.
Cell types and diseases have been clustered using complete-linkage
hierarchical clustering and reordered accordingly. (PDF 2960 kb )
Additional file 10: Table S5. Disease–cell-type association P values
computed using the GSC method. (XLS 686 kb )
Additional file 11: Table S6. Disease-associated cell types identified by
the GSC method with an FDR of 10 %. DISEASE: Disease name. CELL_TYPE:
Cell-type name. P_VALUE: Unadjusted P value computed using the GSC
method. Q_VALUE: Minimum FDR at which the association can be declared
Cornish et al. Genome Medicine (2015) 7:95
significant, computed using the Benjamini–Hochberg procedure for
multiple testing. SIG_GSO: Whether the association is declared significant
using the GSO method at an FDR of 10 %. SIG_TEXT: Whether the
association is declared significant using text-mining at an FDR of 10 %. (XLS
78 kb)
Additional file 12: Table S7. Term mappings for the cell types used in
the analyses. NAME: Cell-type name used throughout the analyses (derived
from the FANTOM5 project sample name). CL_ID: Cell ontology identifier
mapped to the cell type. MESH: The one or two MeSH terms mapped to
the cell type used to conduct the text-mining. (XLS 14 kb)
Additional file 13: Table S8. Term mappings for the diseases used in the
analyses. NAME: Disease name used throughout the analyses (obtained fro
m DisGeNET). UMLS: UMLS identifiers mapped to the disease. MESH: MeSH
term mapped to the disease used to conduct the text-mining. (XLS 66 kb )
Additional file 14: Table S9. Disease–cell-type association P values
computed using the text-mining method. (XLS 438 kb )
Additional file 15: Table S10. Disease–cell-type association P values
computed using the GSO method. (XLS 686 kb )
Additional file 16: Table S11. Disease-associated cell types identified by
the GSO method with an FDR of 10 %. DISEASE: Disease name. CELL_TYPE:
Cell-type name. P_VALUE: Unadjusted P value computed using the GSO
method. Q_VALUE: Minimum FDR at which the association can be
declared significant, computed using the Benjamini–Hochberg procedure
for multiple testing. SIG_GSC: Whether the association is declared
significant using the GSC method at an FDR of 10 %. SIG_TEXT: Whether
the association is declared significant using text-mining at an FDR of 10 %.
(XLS 69 kb )
Additional file 17: Figure S6. Heat map comparing the disease–cell-type
associations identified by the GSC method and text-mining. Differences are
compared by first correcting the P values for multiple testing using the
Benjamini–Hochberg procedure. The − log10 of the text-mined q value is
then subtracted from the − log10 of the GSC method-computed q value. If
the GSC method identifies the association as more significant, the
corresponding cell is colored red. If text-mining identifies the association as
more significant, the cell is colored blue. If the significance level is similar
between methods, the cell is colored white. (PDF 3190 kb )
Additional file 18: Figure S7. Venn diagrams of the disease–cell-type
associations (A) supported and (B) not supported by text-mining that are
also identified by the GSC and GSO methods. Sets of associations were
produced by applying an FDR cutoff of 10 % to the GSC, GSO and
text-mining results. (PDF 677 kb)
Additional file 19: Table S12. The number of disease–cell-type associations
identified by the GSC and GSO methods supported by text-mining at FDRs
of 5, 10 and 20 %. Precision, recall and the F1 score are computed using
associations identified by the text-mining method as true positives.
Overlap significance was measured using Fisher’s exact test. (XLS 7.50 kb)
Additional file 20: Figure S8. The proportion of disease–cell-type
associations identified by the GSC and GSO methods supported by
text-mining at various cutoffs. Disease–cell-type associations were ranked
by their q value. The number of associations represents the size of the set
of the top-ranked disease–cell-type associations. To compute the
proportion of disease–cell-type associations supported by text-mining, it
was necessary to apply an FDR cutoff to the text-mining results. Here we
use three cutoffs: (A) 10 %, (B) 1 % and (C) 0.1 %. The GSC method
assigned 219 associations the lowest-possible P value, while the GSO
method assigned 211 associations this P value. It was not possible to order
these top-ranked associations and therefore the GSC and GSO curves start
at 219 and 211 respectively. (PDF 706 kb )
Additional file 21: Figure S9. Histograms showing the proportions of
disease-associated cell types identified by one method supported by
another. An FDR cutoff of 10 % was applied to the results of each method
to produce sets of disease–cell-type associations. For each disease, the
proportion of associations identified by method m1 supported by method
m2 was computed by dividing the number of associations identified by
method m1 by the number of associations identified by both method m1
and m2 . A value of 1 indicates that all associations identified by method m1
are supported by method m2 and a value of 0 indicates that no
associations identified by method m1 are supported by method m2 .
Page 16 of 18
Diseases where both methods identified no associated cell types were
removed. There is a large amount of support between the GSC and GSO
methods. For 64.0 % of diseases, at least 50.0 % of associations identified by
the GSC method are supported by the GSO method. Similarly, for 64.8 % of
diseases, at least 50.0 % of associations identified by the GSO method are
supported by the GSC method. (PDF 1050 kb )
Abbreviations
CAGE: cap analysis of gene expression; FDR: false discovery rate; GSC: gene set
compactness; GSO: gene set overexpression; JSD: Jensen–Shannon distance;
MeSH: medical subject heading; MS: multiple sclerosis; PPI: protein–protein
interaction; RWR: random walk with restart; TSS: transcription start site; UMLS:
United Medical Language System.
Competing interests
MJES is a director and shareholder in Equinox Pharma Ltd, which uses
bioinformatics and chemoinformatics in drug discovery research and services.
The remaining authors declare that they have no competing interests.
Authors’ contributions
AJC, IF, AD and MJES designed the research. AJC and IF performed the
research. AJC and IF analyzed the data. AJC, IF, AD and MJES wrote the paper.
All authors read and approved the final manuscript.
Acknowledgements
This work is supported by a British Heart Foundation PhD studentship (AJC),
the Biotechnology and Biological Sciences Research Council (IF) and the
Medical Research Council (AD).
Received: 12 February 2015 Accepted: 31 July 2015
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